Visualizing laser ablation using plasma imaging and deep learning
Visualizing laser ablation using plasma imaging and deep learning
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DOI：
10.1364/optcon.495923
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
J. Grant-Jacob;B. Mills;M. Zervas
中科院分区：
文献类型：
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作者：
J. Grant-Jacob;B. Mills;M. Zervas
高功率激光烧蚀会导致等离子体的产生和明亮的光的发射，这会妨碍对工件的直接观察。因此，在激光加工过程中能够使样品可视化的替代技术是令人感兴趣的。在这里，我们表明，在激光烧蚀过程中产生的等离子体，当垂直于样品表面观察时，包含有关样品外观的信息。具体来说，我们证明了深度学习可以直接从单脉冲飞秒激光烧蚀过程中产生的等离子体的2D投影图像预测样品的2D外观。此外，该方法还能够识别用于加工样品的最近激光脉冲的脉冲能量。这项工作可以在研究和工业中的激光材料加工中应用，在激光烧蚀过程中需要实时可视化样品表面的情况下。
High power laser ablation can lead to the creation of plasma and the emission of bright light, which can prevent the direct observation of the workpiece. Alternative techniques for enabling the visualization of the sample during laser machining are therefore of interest. Here, we show that the plasma created during laser ablation, when viewed perpendicular to the sample surface, contains information regarding the appearance of the sample. Specifically, we show that deep learning can predict the 2D appearance of the sample, directly from 2D projected images of the plasma produced during single pulse femtosecond laser ablation. In addition, this approach also enables the identification of the pulse energy of the most recent laser pulse used to machine the sample. This work could have applications across laser materials processing in research and industry, in cases where there is a requirement for real-time visualization of the sample surface during laser ablation.